scholarly journals Effects of Water Stress on Leaf Temperature and Chlorophyll Fluorescence Parameters in Cotton and Peanut

2010 ◽  
Vol 13 (3) ◽  
pp. 269-278 ◽  
Author(s):  
Shahenshah ◽  
Akihiro Isoda
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Stress ◽  
Leaf Temperature ◽  
Chlorophyll Fluorescence Parameters ◽  
Fluorescence Parameters

scholarly journals Silicon and boron mitigate the effects of water deficit on sunflower

2019 ◽  
Vol 23 (3) ◽  
pp. 175-182 ◽  
Author(s):  
Jose M. G. Neves ◽  
Leonardo A. de Aquino ◽  
Paulo G. Berger ◽  
Júlio C. L. Neves ◽  
Genelício C. Rocha ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Stress ◽  
Experimental Design ◽  
Water Deficit ◽  
Magnesium Silicate ◽  
Dolomitic Limestone ◽  
Chlorophyll Fluorescence Parameters ◽  
Fluorescence Parameters ◽  
Calcium Magnesium ◽  
Beginning Of Flowering

ABSTRACT The objective of this study was to evaluate the effect of Ca and Mg silicate and B on the gas exchange, leaf water potential and chlorophyll fluorescence parameters in the sunflower variety Embrapa 122 -V2000 under water stress conditions. The trial was conducted in Red Yellow Latosol with very clayey texture, with contrasting levels of Si and B and subjected to water deficit. The experimental design was randomized blocks in a 24 factorial arrangement, with five replicates. Treatments consisted of the combination of two acidity corrective agents (calcium magnesium silicate and dolomitic limestone), two levels of base saturation (30 and 70%), two levels of B (0.18 mg dm-3 - value available in the soil and 1.20 mg dm-3) and two water regimes (with and without water stress from the beginning of flowering). It can be concluded that the supply of Si and B can reduce the damage to sunflower caused by water stress.

Effects of water stress and light intensity on chlorophyll fluorescence parameters and pigments of Aloe vera L.

2016 ◽  
Vol 106 ◽  
pp. 141-148 ◽  
Author(s):  
Saeid Hazrati ◽  
Zeinolabedin Tahmasebi-Sarvestani ◽  
Seyed Ali Mohammad Modarres-Sanavy ◽  
Ali Mokhtassi-Bidgoli ◽  
Silvana Nicola
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Stress ◽  
Light Intensity ◽  
Aloe Vera ◽  
Chlorophyll Fluorescence Parameters ◽  
Fluorescence Parameters

scholarly journals Some growth and chlorophyll fluorescence parameters of black and hybrid poplar clones under water stress

2017 ◽  
Vol 41 ◽  
pp. 348-356 ◽  
Author(s):  
Akkın SEMERCİ ◽  
Nuran ÇİÇEK ◽  
F. Alptekin KARAHAN ◽  
Ercan ÖZYÜREK ◽  
Yeliz KAŞKO ARICI ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Stress ◽  
Hybrid Poplar ◽  
Chlorophyll Fluorescence Parameters ◽  
Fluorescence Parameters ◽  
Poplar Clones

scholarly journals Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Pawan Kumar ◽  
Renee L. Eriksen ◽  
Ivan Simko ◽  
Beiquan Mou
Keyword(s):  
Machine Learning ◽  
Chlorophyll Fluorescence ◽  
Water Stress ◽  
Hyperspectral Imaging ◽  
Classification Accuracy ◽  
Vegetation Indices ◽  
Chlorophyll Fluorescence Parameters ◽  
Fluorescence Parameters ◽  
Water Stress Tolerance ◽  
Horticultural Traits

Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce (Lactuca sativa L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency. Water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a L. sativa × L. serriola interspecific recombinant inbred line (RIL) population. A total of 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits and 34 QTL for spectral vegetation indices (VI) were identified. The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5%, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26% while the PV explained by the VI QTL ranged from 7.2 to 17.19%. Eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI were identified on six lettuce chromosomes. Molecular markers linked to the mapped QTL clusters can be targeted for marker-assisted selection to develop water-stress tolerant lettuce.

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